Towards Effective Next POI Prediction: Spatial and Semantic Augmentation with Remote Sensing Data

• URL: http://arxiv.org/abs/2404.04271v1
• Date: Fri, 22 Mar 2024 04:22:36 GMT
• Title: Towards Effective Next POI Prediction: Spatial and Semantic Augmentation with Remote Sensing Data
• Authors: Nan Jiang, Haitao Yuan, Jianing Si, Minxiao Chen, Shangguang Wang,
• Abstract summary: We propose an effective deep-learning method within a two-step prediction framework. Our method first incorporates remote sensing data, capturing pivotal environmental context. We construct the QR-P graph for the user's historical trajectories to encapsulate historical travel knowledge.
• Score: 10.968721742000653
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The next point-of-interest (POI) prediction is a significant task in location-based services, yet its complexity arises from the consolidation of spatial and semantic intent. This fusion is subject to the influences of historical preferences, prevailing location, and environmental factors, thereby posing significant challenges. In addition, the uneven POI distribution further complicates the next POI prediction procedure. To address these challenges, we enrich input features and propose an effective deep-learning method within a two-step prediction framework. Our method first incorporates remote sensing data, capturing pivotal environmental context to enhance input features regarding both location and semantics. Subsequently, we employ a region quad-tree structure to integrate urban remote sensing, road network, and POI distribution spaces, aiming to devise a more coherent graph representation method for urban spatial. Leveraging this method, we construct the QR-P graph for the user's historical trajectories to encapsulate historical travel knowledge, thereby augmenting input features with comprehensive spatial and semantic insights. We devise distinct embedding modules to encode these features and employ an attention mechanism to fuse diverse encodings. In the two-step prediction procedure, we initially identify potential spatial zones by predicting user-preferred tiles, followed by pinpointing specific POIs of a designated type within the projected tiles. Empirical findings from four real-world location-based social network datasets underscore the remarkable superiority of our proposed approach over competitive baseline methods.

Related papers

• Self-supervised Graph-based Point-of-interest Recommendation [66.58064122520747]
  Next Point-of-Interest (POI) recommendation has become a prominent component in location-based e-commerce. We propose a Self-supervised Graph-enhanced POI Recommender (S2GRec) for next POI recommendation. In particular, we devise a novel Graph-enhanced Self-attentive layer to incorporate the collaborative signals from both global transition graph and local trajectory graphs.
  arXiv  Detail & Related papers  (2022-10-22T17:29:34Z)
• Conditioned Human Trajectory Prediction using Iterative Attention Blocks [70.36888514074022]
  We present a simple yet effective pedestrian trajectory prediction model aimed at pedestrians positions prediction in urban-like environments. Our model is a neural-based architecture that can run several layers of attention blocks and transformers in an iterative sequential fashion. We show that without explicit introduction of social masks, dynamical models, social pooling layers, or complicated graph-like structures, it is possible to produce on par results with SoTA models.
  arXiv  Detail & Related papers  (2022-06-29T07:49:48Z)
• Handling Distribution Shifts on Graphs: An Invariance Perspective [77.14319095965058]
  We formulate the OOD problem for node-level prediction on graphs. We develop a new domain-invariant learning approach, named Explore-to-Extrapolate Risk Minimization. We prove the validity of our method by theoretically showing its guarantee of a valid OOD solution.
  arXiv  Detail & Related papers  (2022-02-05T02:31:01Z)
• Masked Transformer for Neighhourhood-aware Click-Through Rate Prediction [74.52904110197004]
  We propose Neighbor-Interaction based CTR prediction, which put this task into a Heterogeneous Information Network (HIN) setting. In order to enhance the representation of the local neighbourhood, we consider four types of topological interaction among the nodes. We conduct comprehensive experiments on two real world datasets and the experimental results show that our proposed method outperforms state-of-the-art CTR models significantly.
  arXiv  Detail & Related papers  (2022-01-25T12:44:23Z)
• Cyclic Graph Attentive Match Encoder (CGAME): A Novel Neural Network For OD Estimation [8.398623478484248]
  Origin-Destination Estimation plays an important role in traffic management and traffic simulation in the era of Intelligent Transportation System (ITS) Previous model-based models face the under-determined challenge, thus desperate demand for additional assumptions and extra data exists. We propose Cyclic Graph Attentive Matching (C-GAME) based on a novel Graph Matcher with double-layer attention mechanism.
  arXiv  Detail & Related papers  (2021-11-26T08:57:21Z)
• Trajectory Prediction with Graph-based Dual-scale Context Fusion [43.51107329748957]
  We present a graph-based trajectory prediction network named the Dual Scale Predictor. It encodes both the static and dynamical driving context in a hierarchical manner. Thanks to the proposed dual-scale context fusion network, our DSP is able to generate accurate and human-like multi-modal trajectories.
  arXiv  Detail & Related papers  (2021-11-02T13:42:16Z)
• MobTCast: Leveraging Auxiliary Trajectory Forecasting for Human Mobility Prediction [5.911865723926626]
  We propose MobTCast, a Transformer-based context-aware network for mobility prediction. We explore the influence of four types of context in the mobility prediction: temporal, semantic, social and geographical contexts. In our experimental results, MobTCast outperforms other state-of-the-art next POI prediction methods.
  arXiv  Detail & Related papers  (2021-09-30T09:42:15Z)
• SIRI: Spatial Relation Induced Network For Spatial Description Resolution [64.38872296406211]
  We propose a novel relationship induced (SIRI) network for language-guided localization. We show that our method is around 24% better than the state-of-the-art method in terms of accuracy, measured by an 80-pixel radius. Our method also generalizes well on our proposed extended dataset collected using the same settings as Touchdown.
  arXiv  Detail & Related papers  (2020-10-27T14:04:05Z)
• Learning Geo-Contextual Embeddings for Commuting Flow Prediction [20.600183945696863]
  Predicting commuting flows based on infrastructure and land-use information is critical for urban planning and public policy development. Conventional models, such as gravity model, are mainly derived from physics principles and limited by their predictive power in real-world scenarios. We propose Geo-contextual Multitask Embedding Learner (GMEL), a model that captures the spatial correlations from geographic contextual information for commuting flow prediction.
  arXiv  Detail & Related papers  (2020-05-04T17:45:18Z)
• Learning to Predict Context-adaptive Convolution for Semantic Segmentation [66.27139797427147]
  Long-range contextual information is essential for achieving high-performance semantic segmentation. We propose a Context-adaptive Convolution Network (CaC-Net) to predict a spatially-varying feature weighting vector. Our CaC-Net achieves superior segmentation performance on three public datasets.
  arXiv  Detail & Related papers  (2020-04-17T13:09:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.